Over the past few years powerful new methods have been devised that enable researchers to simultaneously study the dynamics of expression of an entire genome. This potentially vast quantity of data enables, in principle, the dissection of the complex genetic networks that control the patterns and rhythms of gene expression in the cell. The goal of the proposed work is to develop a systems analysis of whole genome expression profiles. Our aim is to infer regulatory networks from gene expression data and to validate these procedures with experimental results. To this end, the project will integrate a combination of efforts involving: systems modeling and visualization of large networks, experimentation on model systems, and development of database and bioinformatic tools. Our strategy for identifying genotypes associated with complex phenotype is to use a comparative analysis of time series data for normal and functional inactivated systems. The functional inactivation is achieved at the individual gene level using a variety of genetic and pharmacological methods. The key to the success of such an approach is the development of dynamic, predictive models of gene expression. The aim of this proposal is to develop the computational and informatic tools to analyze and interpret expression profiles associated with these functional modifications. These tools will be tested against a validated "gold-standard" data set. This model system will involve profiling signal transduction pathways in SW1353 chondrosarcoma cells. Integral to this effort is the interplay between statistical analysis and experimental design. Such interplay of efforts is made possible by the collaborations built into the project.